Generate high-quality chart images from data, supporting common types such as bar charts, line charts, pie charts, and scatter plots.
Use Cases
- The user says "turn this data into a chart" or "draw a bar chart"
- The user provides Excel/CSV data that needs to be visualized
- Works with excel-analyzer to automatically generate charts after analysis
- The user says "make a sales trend chart" or "draw a pie chart to see the proportions"
Execution Method
Basic Usage
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
# 中文字体支持
plt.rcParams['font.sans-serif'] = ['PingFang SC', 'Microsoft YaHei', 'SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False
fig, ax = plt.subplots(figsize=(10, 6))
# 示例：柱状图
categories = ['一月', '二月', '三月', '四月']
values = [120, 150, 180, 200]
ax.bar(categories, values, color='#4A90D9')
ax.set_title('月度销售额', fontsize=16, fontweight='bold')
ax.set_ylabel('金额（万元）')
plt.tight_layout()
plt.savefig('/tmp/chart.png', dpi=150, bbox_inches='tight')
plt.close()
Supported Chart Types
| Type | Method | Applicable Scenario |
|---|---|---|
| Bar Chart | ax.bar() |
Category comparison |
| Line Chart | ax.plot() |
Trend changes |
| Pie Chart | ax.pie() |
Proportion analysis |
| Scatter Plot | ax.scatter() |
Correlation analysis |
| Horizontal Bar Chart | ax.barh() |
Ranking comparison |
| Stacked Chart | ax.bar(bottom=) |
Composition analysis |
Style Guidelines
- Default color scheme:
#4A90D9(blue),#E85D75(red),#50C878(green),#F5A623(orange) - Resolution: 150 DPI (screen viewing) or 300 DPI (printing)
- Chinese titles and labels must have a Chinese font set
- Place the legend where it does not obscure the data
Output Guidelines
- Save the chart as a PNG file to a temporary directory
- Return the file path for the user to view or embed in a report
- Automatically select the most suitable chart type (when the user does not specify one)
- Automatically truncate or aggregate when the data volume is large to avoid a crowded chart